Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/1556
Title: Concurrent artificial neural network modeling of single-crystal and bulk-ceramics ferroelectric-hysteresis: An application to barium titanate
Authors: Laosiritaworn W.
Wongdamnern N.
Yimnirun R.
Laosiritaworn Y.
Issue Date: 2011
Abstract: This paper proposed an application of Artificial Neural Network (ANN) to concurrently model ferroelectric hysteresis properties of Barium Titanate in both single-crystal and bulk-ceramics forms. In the ANN modeling, there are 3 inputs, which are type of materials (single or bulk), field amplitude and frequency, and 1 output, which is hysteresis area. Appropriate number of hidden layer and hidden node were achieved through a search of up to 2 layers and 30 neurons in each layer. After ANN had been properly trained, a network with highest accuracy was selected. Query file of unseen input data was then input to the selected network to obtain the predicted hysteresis area. From the results, the target and predicted data were found to match very well. This therefore suggests that ANN can be successfully used to concurrently model ferroelectric hysteresis property even though the considered ferroelectrics are with different domains, grains and microscopic crystal structures. Copyright © Taylor &Francis Group, LLC.
URI: http://www.scopus.com/inward/record.url?eid=2-s2.0-79960719572&partnerID=40&md5=c87619940f8badd5a436afb209bc97c0
http://cmuir.cmu.ac.th/handle/6653943832/1556
ISSN: 150193
Appears in Collections:ENG: Journal Articles

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